Pejman Ebrahimzadeh, Luis Borja Peral Martínez, Inés Fernández Pariente, Francisco Javier Belzunce Varela
{"title":"通过响应面方法 (RSM) 优化 AISI 316L 钢的喷丸强化参数:引入两个新的机械方面","authors":"Pejman Ebrahimzadeh, Luis Borja Peral Martínez, Inés Fernández Pariente, Francisco Javier Belzunce Varela","doi":"10.1007/s00170-024-13274-8","DOIUrl":null,"url":null,"abstract":"<p>The study explores the application of shot-peening (SP) on AISI 316L stainless steel to enhance mechanical properties. It focuses on optimizing SP parameters—coverage percentage (C) ranging from 100 to 4500% and shot velocity (P) between 1.5 and 6 bar while other SP factors were maintained constant—using response surface methodology (RSM) entails creating a mathematical model to analyze data accurately. This model explores interactions among initial configurations to optimize mechanical properties and enhance the performance of the current steel after the SP surface treatment. These properties evaluated include cumulative compressive residual stress (CCRS), cumulative full-width at half-maximum (CFWHM) newfangled factors for researchers to analyze, austenite transformation to martensite, micro-hardness, and surface roughness. Through the RSM model, increasing <i>P</i> leads to an increase in all response values in each one, except for microhardness, which registers a minor decrease from 1.5 to 6 bar. Elevating <i>C</i> promotes responses, excluding roughness, decreasing until 2300% and reaching its minimum. At 4500% <i>C</i>, roughness peaks, exceeding the initial amount at 100% <i>C</i>. In the optimization section, it seeks a passable value for each parameter. Desired responses involve maximizing CCRS, CFWHM, and micro-hardness while minimizing martensite and roughness. For interactions in all responses, at <i>P</i> = 6 bar and <i>C</i> = 1860%, values for each response were CCRS = 218 (MPa.mm), CFWHM = 0.6871 (°.mm), micro-hardness = 394 (HV), martensite conversion = 48 (%), and roughness = 5.45 (µm). Response reassessment in the real tests by comparison RSM model in optimal points showed a minimum error of 4.05 for roughness and a maximum error of 12.09 for CCRS. Other responses contained errors between this spectrum.</p>","PeriodicalId":50345,"journal":{"name":"International Journal of Advanced Manufacturing Technology","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of shot-peening parameters for steel AISI 316L via response surface methodology (RSM): introducing two novel mechanical aspects\",\"authors\":\"Pejman Ebrahimzadeh, Luis Borja Peral Martínez, Inés Fernández Pariente, Francisco Javier Belzunce Varela\",\"doi\":\"10.1007/s00170-024-13274-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The study explores the application of shot-peening (SP) on AISI 316L stainless steel to enhance mechanical properties. It focuses on optimizing SP parameters—coverage percentage (C) ranging from 100 to 4500% and shot velocity (P) between 1.5 and 6 bar while other SP factors were maintained constant—using response surface methodology (RSM) entails creating a mathematical model to analyze data accurately. This model explores interactions among initial configurations to optimize mechanical properties and enhance the performance of the current steel after the SP surface treatment. These properties evaluated include cumulative compressive residual stress (CCRS), cumulative full-width at half-maximum (CFWHM) newfangled factors for researchers to analyze, austenite transformation to martensite, micro-hardness, and surface roughness. Through the RSM model, increasing <i>P</i> leads to an increase in all response values in each one, except for microhardness, which registers a minor decrease from 1.5 to 6 bar. Elevating <i>C</i> promotes responses, excluding roughness, decreasing until 2300% and reaching its minimum. At 4500% <i>C</i>, roughness peaks, exceeding the initial amount at 100% <i>C</i>. In the optimization section, it seeks a passable value for each parameter. Desired responses involve maximizing CCRS, CFWHM, and micro-hardness while minimizing martensite and roughness. For interactions in all responses, at <i>P</i> = 6 bar and <i>C</i> = 1860%, values for each response were CCRS = 218 (MPa.mm), CFWHM = 0.6871 (°.mm), micro-hardness = 394 (HV), martensite conversion = 48 (%), and roughness = 5.45 (µm). Response reassessment in the real tests by comparison RSM model in optimal points showed a minimum error of 4.05 for roughness and a maximum error of 12.09 for CCRS. Other responses contained errors between this spectrum.</p>\",\"PeriodicalId\":50345,\"journal\":{\"name\":\"International Journal of Advanced Manufacturing Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Advanced Manufacturing Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00170-024-13274-8\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Manufacturing Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00170-024-13274-8","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Optimization of shot-peening parameters for steel AISI 316L via response surface methodology (RSM): introducing two novel mechanical aspects
The study explores the application of shot-peening (SP) on AISI 316L stainless steel to enhance mechanical properties. It focuses on optimizing SP parameters—coverage percentage (C) ranging from 100 to 4500% and shot velocity (P) between 1.5 and 6 bar while other SP factors were maintained constant—using response surface methodology (RSM) entails creating a mathematical model to analyze data accurately. This model explores interactions among initial configurations to optimize mechanical properties and enhance the performance of the current steel after the SP surface treatment. These properties evaluated include cumulative compressive residual stress (CCRS), cumulative full-width at half-maximum (CFWHM) newfangled factors for researchers to analyze, austenite transformation to martensite, micro-hardness, and surface roughness. Through the RSM model, increasing P leads to an increase in all response values in each one, except for microhardness, which registers a minor decrease from 1.5 to 6 bar. Elevating C promotes responses, excluding roughness, decreasing until 2300% and reaching its minimum. At 4500% C, roughness peaks, exceeding the initial amount at 100% C. In the optimization section, it seeks a passable value for each parameter. Desired responses involve maximizing CCRS, CFWHM, and micro-hardness while minimizing martensite and roughness. For interactions in all responses, at P = 6 bar and C = 1860%, values for each response were CCRS = 218 (MPa.mm), CFWHM = 0.6871 (°.mm), micro-hardness = 394 (HV), martensite conversion = 48 (%), and roughness = 5.45 (µm). Response reassessment in the real tests by comparison RSM model in optimal points showed a minimum error of 4.05 for roughness and a maximum error of 12.09 for CCRS. Other responses contained errors between this spectrum.
期刊介绍:
The International Journal of Advanced Manufacturing Technology bridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration.